Reinforcement Learning Method for Identifying Health Issues for People with Chronic Diseases

Healthcare systems will inevitably be tasked with determining the best method for identifying and modelling the risks associated with chronic diseases. For these reasons, it is thought that clinically examining medical records using traditional and machine learning technologies could be a beneficial, real, and most crucially cost-effective alternative to human medical professionals. Understanding the significance of early detection in preventing the worst effects of such diseases is crucial. Manually diagnosing diseases is often a time-consuming and inaccurate process for doctors; from de-identified healthcare data and narrative texts, information is extracted. This system has been used to overcome the problem of missing information using both structured data (SD) and unstructured data (USD) by the reinforcement learning-based multimodal risk identification system (RL-MRIS). The prediction accuracy and the convergence speed have been analysed for various data sets. It is based on the AutoLearn algorithm (ALA), which can identify a tool for determining elements in a data set and the broad variations of chronic diseases. The prediction accuracy and the convergence speed have been obtained using this identification system.